Learning a distance metric from multi-instance multi-label data

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Volumn , Issue , 2009, Pages 896-902

  Jin, Rong ^a   Wang, Shijun ^b   Zhou, Zhi Hua ^c  

^a Michigan State University   (United States)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

^c NANJING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; ITERATIVE METHODS; OBJECT RECOGNITION;

AUTOMATED IMAGE ANNOTATIONS; DISTANCE METRIC LEARNING; EMPIRICAL STUDIES; ITERATIVE ALGORITHM; LEARNING PROBLEM; MULTI-INSTANCE LEARNING; MULTI-INSTANCE MULTI-LABEL LEARNING; VISUAL OBJECT RECOGNITION;

LEARNING ALGORITHMS;

EID: 70450164149     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206684     Document Type: Conference Paper

References (23)

1. S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In NIPS 15, pages 561-568. 2003.
2. S. P. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
3. P.-M. Cheung and J. T. Kwok. A regularization framework for multiple-instance learning. In ICML, pages 193-200, 2006.
4. T. G. Dietterich, R. H. Lathrop, and T. Lozano- Pérez. Solving the multiple-instance problem with axis-parallel rectangles. AIJ, 89(1-2):31-71, 1997.
5. P. Duygulu, K. Barnard, J. F. G. de Freitas, and D. A. Forsyth. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In ECCV, pages 349-354, 2002.
6. A. Elisseeff and J.Weston. A kernel method for multilabelled classification. In NIPS 14, pages 681-687. 2002.
7. T. Gärtner, P. A. Flach, A. Kowalczyk, and A. J. Smola. Multi-instance kernels. In ICML, pages 179- 186, 2002.
8. O. Maron and T. Lozano-Pérez. A framework for multiple-instance learning. In NIPS 10, pages 570- 576. 1998.
9. S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, 2000.
10. N. Shental, T. Hertz, D. Weinshall, and M. Pavel. Adjustment learning and relevant component analysis. In ECCV, pages 776-792, 2002.
11. J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, 2000.
12. P. Viola, J. Platt, and C. Zhang. Multiple instance boosting for object detection. In NIPS 18, pages 1419- 1426. 2006.
13. J. Wang and J.-D. Zucker. Solving the multi-instance problem: A lazy learning approach. In ICML, pages 1119-1125, 2000.
14. K.Weinberger, J. Blitzer, and L. Saul. Distance metric learning for large margin nearest neighbor classification. In NIPS 18, pages 1473-1480. 2006.
15. E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell. Distance metric learning, with application to clustering with side-information. In NIPS 15, pages 505-512. 2003.
16. X. Xu and E. Frank. Logistic regression and boosting for labeled bags of instances. In PAKDD, pages 272- 281, 2004.
17. L. Yang and R. Jin. Distance metric learning: A comprehensive survey. Technical report, Department of Computer Science & Engineering, Michigan State University, 2006.
18. Z.-J. Zha, X.-S. Hua, T. Mei, J. Wang, G.-J. Qi, and Z. Wang. Joint multi-label multi-instance learning for image classification. In CVPR, 2008.
19. M.-L. Zhang and Z.-H. Zhou. Multi-label learning by instance differentiation. In AAAI, pages 669-674, 2007.
20. M.-L. Zhang and Z.-H. Zhou. M3MIML: A maximum margin method for multi-instance multi-label learning. In ICDM, pages 688-697, 2008.
21. Q. Zhang and S. A. Goldman. EM-DD: An improved multi-instance learning technique. In NIPS 14, pages 1073-1080. 2002.
22. Z.-H. Zhou and M.-L. Zhang. Ensembles of multiinstance learners. In ECML, pages 492-502, 2003.
23. Z.-H. Zhou and M.-L. Zhang. Multi-instance multilabel learning with application to scene classification. In NIPS 19, pages 1609-1616. 2007.